from multiagent.environment import MultiAgentEnv
import multiagent.scenarios as scenarios
import torch
import numpy as np
from agent import DDPGAgent
from maddpg import MADDPG
from utils import MultiAgentReplayBuffer
def make_env(scenario_name, benchmark=False):
# load scenario from script
scenario = scenarios.load(scenario_name + ".py").Scenario()
# create world
world = scenario.make_world()
# create multiagent environment
if benchmark:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward,
scenario.observation, scenario.benchmark_data)
else:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward,
scenario.observation)
return env
env = make_env(scenario_name="simple_spread")
ma_controller = MADDPG(env, 1000000)
ma_controller.run(500, 300, 32)
self.reset()
def reset(self):
self.states = self.env.reset()
self.step = 0
def get_actions(self, states):
actions = []
for i in range(self.num_agents):
action = self.agents[i].get_action(states[i])
actions.append(action)
return actions
def run(self):
for i in range(200):
actions = self.get_actions(self.states)
next_states, rewards, dones, _ = self.env.step(actions)
self.env.render()
if all(dones) or self.step == 199:# cfg.max_episode_len - 1:
self.reset()
break
else:
dones = [0 for _ in range(self.num_agents)]
self.states = next_states
self.step += 1
if __name__ == '__main__':
maddpg = MADDPG()
maddpg.run()
